CN112180324A - Non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring - Google Patents
Non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring Download PDFInfo
- Publication number
- CN112180324A CN112180324A CN202010959291.2A CN202010959291A CN112180324A CN 112180324 A CN112180324 A CN 112180324A CN 202010959291 A CN202010959291 A CN 202010959291A CN 112180324 A CN112180324 A CN 112180324A
- Authority
- CN
- China
- Prior art keywords
- circular
- signal
- vector
- observation
- aerial vehicle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000009467 reduction Effects 0.000 title claims abstract description 37
- 238000012545 processing Methods 0.000 title claims abstract description 21
- 238000012544 monitoring process Methods 0.000 title claims abstract description 19
- 239000013598 vector Substances 0.000 claims abstract description 49
- 239000011159 matrix material Substances 0.000 claims abstract description 40
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 36
- 230000005855 radiation Effects 0.000 claims description 40
- 238000005070 sampling Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 15
- 150000001875 compounds Chemical class 0.000 claims description 11
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 7
- 230000004807 localization Effects 0.000 description 9
- 230000033001 locomotion Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/15—Correlation function computation including computation of convolution operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Software Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
The invention discloses a non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring, which considers non-circular phase information of signals when establishing an algorithm model, and expands a received signal matrix by utilizing the characteristic that the elliptic covariance is not zero so as to achieve the purpose of increasing the spatial degree of freedom; by combining the direct positioning technology and the concept of a Capon algorithm in the direction of arrival estimation, the covariance matrixes of the data received by the positions of the observation stations are comprehensively considered, and the high-precision positioning of the multi-target source can be realized only by one moving observation station; and the problem of high complexity caused by the introduction of the non-circular phase is effectively solved by introducing a dimension reduction idea. The method avoids the problem of matching intermediate parameters and parameters of the traditional two-step method, directly obtains the position information of the target source from the original receiving data layer, and effectively improves the positioning precision; the expansion of the received signal vector increases the spatial degree of freedom of the algorithm, improves the resolution and can simultaneously estimate more information sources.
Description
Technical Field
The invention relates to the technical field of passive wireless positioning, in particular to a non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle movement monitoring.
Background
In the existing radiation source positioning method, radiation source signals are mostly regarded as complex circle Gaussian signals, a signal model is too simple, characteristic information of the radiation source signals cannot be fully utilized, and therefore precision is low. When the signal model is established, the algorithm model is designed in a targeted manner by combining the signal characteristics, so that the positioning accuracy of the algorithm can be improved. Non-circular signals are common signal types in modern communication systems, so that the research on a radiation source positioning method aiming at the non-circular signal type has more general practicability and very important practical significance.
The traditional two-step positioning technology needs to estimate intermediate parameters firstly, the existence of an intermediate processing link causes that the intermediate processing link inevitably loses partial position information, and under multiple radiation sources, extra parameter matching is needed before position resolving, so that the asymptotically optimal estimation performance is difficult to obtain, and the practicability is low. The direct positioning technology directly estimates the position of the radiation source from the original received data without additional parameter estimation, thereby effectively avoiding the problem of a two-step positioning system and having higher positioning precision. The direct positioning technology can conveniently utilize original data information, so that the direct positioning technology combined with signal characteristics can obtain better estimation performance. However, the existing direct positioning technology combining signal characteristics is not generally applicable to signal types such as constant modulus signals and cyclostationary signals, does not consider the problem of dimension reduction, and has high algorithm complexity.
Disclosure of Invention
The invention aims to solve the technical problem of providing a non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle movement monitoring aiming at the defects involved in the background technology.
The invention adopts the following technical scheme for solving the technical problems:
a non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring comprises the following steps:
step 1), an unmanned aerial vehicle receives a plurality of non-circular radiation source signals in L different observation time slots and samples the received signals;
step 2), expanding received signal vectors according to the non-circular characteristics of the signal source, respectively calculating expanded covariance matrixes of received signals of different observation time slots, and constructing a cost function by using a Capon algorithm;
step 3), reducing the dimension of the cost function, and converting the non-circular phase dimension reduction problem into a secondary optimization problem; fusing all the extended covariance matrixes, and constructing a cost function after dimension reduction;
and 4) searching the cost function subjected to the dimension reduction to obtain the position of the non-circular radiation source.
As a further optimization scheme of the non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring, the received signal r of the unmanned aerial vehicle at the kth sampling moment of the l observation time slot in the step 1)l(k) Is composed of
In the formula, rl(k) Is the received signal vector of the kth sampling moment of the l observation time slot, Q is the number of non-circular radiation sources,for the signal manifold vector of the qth target source to the antenna array in the l observation time slot, sl,q(k) The signal waveform of the qth target source at the kth sampling snapshot time in the ith observation time slot is shown,the noise vector of the antenna array at the kth sampling moment of the ith observation time slot is M, the array element number is M, and the noise is assumed to be complex round white Gaussian noise which is independent from the signal.
As a further optimization scheme of the non-circular signal-oriented dimension reduction processing direct positioning method in the unmanned aerial vehicle mobile monitoring, the specific steps of expanding the received signal vector and calculating the expanded covariance matrix of the received signals of different observation time slots in the step 2) are as follows:
step 2.1), expanding a received signal vector according to the characteristics of the maximum non-circular rate signal:
in the formula, cl(k) For the extended received signal vector at the l-th observation slot, Al(p) is the direction matrix of the l-th observation slot,
in order to extend the direction matrix,an extended direction vector for the l-th observation slot, al(pq) Is a vector of the direction of the light,the non-circular phase of the qth radiation source, Q1, 2, …, Q,in the form of a non-circular phase matrix,a real envelope for the source signal vector;
step 2.2), calculating an extended covariance matrix of the received signal of each observation time slot according to the following formula:
in the formula (I), the compound is shown in the specification,for the extended covariance matrix of the l-th observation slot,and K is the sampling fast beat number.
As a further optimization scheme of the non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring, the cost function constructed by using a Capon algorithm in the step 2) is as follows:
in the formula (I), the compound is shown in the specification,for the cost function before the dimensionality reduction,for the extended signal manifold vector of the l-th observation slot in the search,to expand the inverse of the covariance matrix, p is the position vector,non-circular phase.
As a further optimization scheme of the non-circular signal-oriented dimension reduction processing direct positioning method in the unmanned aerial vehicle mobile monitoring, the detailed steps of the step 3) are as follows:
step 3.1), separating position information and noncircular phase information in the expanded received signal vector through matrix conversion:
in the formula (I), the compound is shown in the specification,for the qth spread signal manifold vector,is a position information matrix of the qth radiation source,for the non-circular phase information vector of the q-th radiation source,is the non-circular phase of the qth radiation source;
For unknown parametersThe above equation is a quadratic optimization problem. Let e be [1,0 ]]TThen, thenThe reconstruction optimization problem is then as follows:
solving by adopting a Lagrange multiplier method, and constructing the following functions:
in the formula (I), the compound is shown in the specification,in order to be the objective function, the target function,is a phase vector, JlAnd (p) is a position matrix corresponding to the l-th observation time slot, and lambda is a multiplier. Order the above type is toIs zero, i.e.
ThenWhere μ is the multiplier coefficient, Jl(p)-1Position matrix J corresponding to the l-th observation time slotl(p) the inverse;
Fusing the extended covariance matrix of all observation time slots to construct a reduced-dimension cost function fRD-Capon(p):
As a further optimization scheme of the non-circular signal-oriented dimension reduction processing direct positioning method in the unmanned aerial vehicle mobile monitoring, the detailed steps of the step 4) are as follows:
and searching the cost function after dimension reduction, wherein the coordinates corresponding to the Q maximum peak values are the positions of the non-circular radiation sources.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. the non-circular characteristic of the radiation source signal is utilized, and the positioning precision is effectively improved;
2. the degree of freedom of the algorithm is increased, and more information sources can be estimated simultaneously;
3. with higher source resolution.
Drawings
Fig. 1 is a flow chart of an implementation of a non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring provided by the invention;
FIG. 2 is a view of a multi-non-circular source positioning scenario in accordance with the present invention;
FIG. 3 is a positioning scattergram of the method of the present invention;
FIG. 4 is a comparison of the method of the present invention, the conventional Capon direct positioning algorithm and the two-step positioning algorithm without passing array elements;
FIG. 5 is a comparison of the method of the present invention, a conventional Capon direct localization algorithm and a two-step localization algorithm at different signal-to-noise ratios;
FIG. 6 is a comparison of the method of the present invention, the conventional Capon direct positioning algorithm and the two-step positioning algorithm at different snapshots;
FIG. 7 is a comparison chart of the calculation time before and after dimension reduction of the method of the present invention under different snapshot numbers.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the present invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, components are exaggerated for clarity.
As shown in fig. 1, a flow chart of a non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring is shown. The unmanned aerial vehicle receives signals from a plurality of non-circular radiation sources in L different observation time slots, and samples the received signals to obtain a received signal matrix; expanding a received signal matrix by using the characteristic that the elliptic covariance is not zero, calculating an expanded covariance matrix of the received signal, and establishing a cost function by using the concept of a Capon algorithm; reducing the dimension of the cost function, converting the cost function into a secondary optimization problem, and removing the non-circular phase search dimension; and finally, fusing covariance matrixes of L different observation time slot received signals, constructing a cost function after dimensionality reduction, and obtaining the position of the non-circular radiation source through searching, wherein the specific steps are as follows:
step 1: the unmanned aerial vehicle receives a plurality of non-circular radiation source signals at L different observation positions, and samples the received signals:
suppose that Q independent far-field narrow-band non-circular signals are incident to a motion observation platform carrying M-element uniform linear arrays, namely an unmanned aerial vehicle, and target sources are respectively positioned at pq=[xq,yq]T(Q ═ 1,2, …, Q), the observation platform moves along a known trajectory, its multiple source localization scene graph based on non-circular signals is shown in fig. 2, the received signal of the observation platform at the kth (K ═ 1,2, …, L) observation position (K ═ 1,2, …, K) sampling moment is:
in the formula, rl(k) Is the received signal vector of the kth sampling moment of the l observation time slot, Q is the number of non-circular radiation sources,for the signal manifold vector of the qth target source to the antenna array in the l observation time slot, sl,q(k) The signal waveform of the qth target source at the kth sampling snapshot time in the ith observation time slot is shown,the noise vector of the antenna array in the l-th observation time slot is defined, M is the array element number, and the noise is assumed to be complex round white Gaussian noise independent from the signal.
Step 2: according to the non-circular characteristic of a signal source, expanding a received signal vector, respectively calculating an expanded covariance matrix of received signals of different observation time slots, and constructing a cost function by utilizing the idea of a Capon algorithm:
the characteristics of the maximum non-circular rate signal can be obtained as follows:
in the formula, sl(k) For the source signal vector of the l-th observation slot,in the form of a non-circular phase matrix,for the non-circular phase of the q-th radiation source,a real envelope for the source signal vector;
thus, the spread received signal vector is:
in the formula, cl(k) For the extended received signal vector of the l-th observation slot, Al(p) is the direction matrix of the l-th observation slot,in order to extend the direction matrix,an extended direction vector for the l-th observation slot, al(pq) Is a direction vector.
The extended covariance matrix of the received signal for the ith observation slotIn the formula (I), the compound is shown in the specification,and K is the matrix of the expanded received signals of the l-th observation time slot, and the sampling fast beat number is K.
Combining the idea of Capon algorithm in DOA estimation algorithm, the cost function is constructed as follows:
in the formula (I), the compound is shown in the specification,for the cost function before the dimensionality reduction,for the extended signal manifold vector of the l-th observation slot in the search,to expand the inverse of the covariance matrix, p is the position vector,non-circular phase.
And step 3: reducing the dimension of the cost function, and converting the non-circular phase dimension reduction problem into a secondary optimization problem:
in step 2, solving and searching dimensionality of the position of the radiation source is overlarge, reducing dimensionality and solving the dimensionality, rewriting the received signal vector in step 2, and separating position information from noncircular phase information through matrix conversion, wherein the method comprises the following steps:
in the formula,For the qth spread signal manifold vector,is a position information matrix of the qth radiation source,for the non-circular phase information vector of the q-th radiation source,is the non-circular phase of the qth radiation source;
For unknown parametersThe above equation is a quadratic optimization problem. Let e be [1,0 ]]TThen, thenThe reconstruction optimization problem is then as follows:
adopting Lagrange multiplier method to solve and construct the following function
In the formula (I), the compound is shown in the specification,in order to be the objective function, the target function,is a phase vector, JlAnd (p) is a position matrix corresponding to the l-th observation time slot, and lambda is a multiplier. Order the above type is toIs zero, i.e.
ThenMu is the multiplier coefficient, Jl(p)-1Position matrix J corresponding to the l-th observation time slotl(p) the inverse;
Fusing all the extended covariance matrixes to construct the matrix with reduced dimensionCost function fRD-Capon(p):
And searching the position of the cost function, wherein the coordinates corresponding to the Q maximum peak values are the position of the non-circular radiation source.
The performance of the method of the invention is better than that of the traditional algorithm through simulation verification. Simulation analysis was performed using MATLAB with Root Mean Square Error (RMSE) as a criterion for evaluating performance, which is defined as follows:
wherein Q is the number of non-circular signal sources, MN is the number of Monte Carlo simulation experiments,is an estimate of the location of the target source, (x)q,yq) Is the actual value of the target source location.
FIG. 3 is a positioning scattergram of the method of the present invention, wherein the number of radiation sources Q-3 is located at p1=[-800,800]、p2=[0,500]And p3=[800,700](unit is m, the same below), non-circular phaseThe unmanned aerial vehicle moves along a known track, a uniform linear array with the array element number M being 6 is mounted, 5 observation positions are respectively (-1000, -500), (-500 ), (0, -500), (500, -500) and (1000, -500), the sampling fast beat number K of each observation position is 100, and the signal-to-noise ratio is 5 dB. It can be seen from the figure that the present invention is effective in achieving simultaneous positioning of multiple non-circular radiation sources.
Fig. 4 is a comparison chart of the method of the present invention, the conventional Capon direct localization algorithm, and the conventional two-step localization algorithm without the array element number. Assuming that the number of radiation sources Q is 3, each is located at p1=[-800,800]、p2=[0,500]And p3=[800,700](unit is m, the same below), non-circular phaseThe unmanned aerial vehicle moves along a known track, the number of array elements of the mounted uniform linear array is respectively 3, 5, 7 and 9, 5 observation positions are respectively (-1000, -500), (-500 ), (0, -500), (500, -500) and (1000, -500), the sampling fast beat number K of each observation position is 100, and the signal-to-noise ratio is 20 dB. As can be seen from the figure, the invention can still realize the positioning under the condition that the number of the array elements is equal to the number of the radiation sources, and the positioning precision is higher than that of the traditional Capon direct positioning algorithm and the two-step positioning algorithm.
FIG. 5 is a comparison graph of the method of the present invention, a conventional Capon direct localization algorithm, and a conventional two-step localization algorithm at different signal-to-noise ratios. Assuming that the number of radiation sources Q is 3, each is located at p1=[-800,800]、p2=[0,500]And p3=[800,700](unit is m, the same below), non-circular phaseThe unmanned aerial vehicle moves along a known track, a uniform linear array with the array element number of 6 is mounted, 5 observation positions are respectively (-1000, -500), (-500 ), (0, -500), (500, -500) and (1000, -500), the sampling fast beat number K of each observation position is 200, and the signal-to-noise ratio is stepped from-5 dB to 30dB at intervals of 5 dB. As can be seen from the figure, the positioning performance of the method is always superior to that of the traditional Capon direct positioning algorithm and the two-step positioning algorithm as the signal-to-noise ratio is increased.
FIG. 6 is a comparison of the method of the present invention, a traditional Capon direct localization algorithm, and a traditional two-step localization algorithm at different snapshots. Assuming that the number of radiation sources Q is 3, each is located at p1=[-800,800]、p2=[0,500]And p3=[800,700](unit is m, the same below), non-circular phaseThe unmanned aerial vehicle moves along a known track, the number of the mounted array elements is 6, and the number of the mounted array elements is 5, namely (-1000, -500), (-500, -5) observation positions00) (0, -500), (500, -500) and (1000, -500), the number of fast beats of samples for each observation position is stepped from 50 to 300 at 50 intervals, with a signal-to-noise ratio of 10 dB. As can be seen from the figure, with the increase of the number of snapshots, the positioning performance of the invention is continuously improved, and the positioning error is always smaller than that of the traditional Capon direct positioning algorithm and the two-step positioning algorithm.
FIG. 7 is a comparison chart of the method of the present invention before and after dimension reduction under different snapshot numbers. Assuming that the number of radiation sources Q is 3, each is located at p1=[-800,800]、p2=[0,500]And p3=[800,700](unit is m, the same below), non-circular phaseThe unmanned aerial vehicle moves along a known track, a uniform linear array with 6 array elements is mounted, 5 observation positions are respectively (-1000, -500), (-500 ), (0, -500), (500, -500) and (1000, -500), the sampling fast beat number of each observation position is from 50 to 300 in 50 intervals, and the signal-to-noise ratio is 20 dB. As can be seen from the figure, the dimension reduction method can effectively reduce the complexity of the algorithm and improve the practicability of the algorithm.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only illustrative of the present invention and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring is characterized by comprising the following steps:
step 1), an unmanned aerial vehicle receives a plurality of non-circular radiation source signals in L different observation time slots and samples the received signals;
step 2), expanding received signal vectors according to the non-circular characteristics of the signal source, respectively calculating expanded covariance matrixes of received signals of different observation time slots, and constructing a cost function by using a Capon algorithm;
step 3), reducing the dimension of the cost function, and converting the non-circular phase dimension reduction problem into a secondary optimization problem; fusing all the extended covariance matrixes, and constructing a cost function after dimension reduction;
and 4) searching the cost function subjected to the dimension reduction to obtain the position of the non-circular radiation source.
2. The method for non-circular signal-oriented dimension reduction processing direct positioning in unmanned aerial vehicle mobile monitoring according to claim 1, wherein in step 1), the received signal r of the unmanned aerial vehicle at the kth sampling moment of the ith observation time slotl(k) Is composed of
In the formula, rl(k) Is the received signal vector of the kth sampling moment of the l observation time slot, Q is the number of non-circular radiation sources,for the signal manifold vector of the qth target source to the antenna array in the l observation time slot, sl,q(k) The signal waveform of the qth target source at the kth sampling snapshot time in the ith observation time slot is shown,the noise vector of the antenna array for the kth sampling instant of the l-th observation slot,m is the array element number, and the noise is assumed to be complex round white Gaussian noise independent from the signal.
3. The non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring according to claim 2, wherein the specific steps of expanding the received signal vector and calculating the expanded covariance matrix of the received signals of different observation time slots in step 2) are as follows:
step 2.1), expanding a received signal vector according to the characteristics of the maximum non-circular rate signal:
in the formula, cl(k) For the extended received signal vector at the l-th observation slot, Al(p) is the direction matrix of the l-th observation slot,in order to extend the direction matrix,an extended direction vector for the l-th observation slot, al(pq) Is a vector of the direction of the light,the non-circular phase of the qth radiation source, Q1, 2, …, Q,in the form of a non-circular phase matrix,a real envelope for the source signal vector;
step 2.2), calculating an extended covariance matrix of the received signal of each observation time slot according to the following formula:
4. The non-circular signal-oriented dimensionality reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring according to claim 3, wherein the cost function constructed by using a Capon algorithm in the step 2) is as follows:
5. The non-circular signal-oriented dimensionality reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring according to claim 4, wherein the detailed steps of the step 3) are as follows:
step 3.1), separating position information and noncircular phase information in the expanded received signal vector through matrix conversion:
in the formula (I), the compound is shown in the specification,for the qth spread signal manifold vector,is a position information matrix of the qth radiation source,for the non-circular phase information vector of the q-th radiation source,is the non-circular phase of the qth radiation source;
For unknown parametersThe above equation is a quadratic optimization problem. Let e be [1,0 ]]TThen, thenThe reconstruction optimization problem is then as follows:
solving by adopting a Lagrange multiplier method, and constructing the following functions:
in the formula (I), the compound is shown in the specification,in order to be the objective function, the target function,is a phase vector, JlAnd (p) is a position matrix corresponding to the l-th observation time slot, and lambda is a multiplier. Order the above type is toIs zero, i.e.
ThenWhere μ is the multiplier coefficient, Jl(p)-1Position matrix J corresponding to the l-th observation time slotl(p) the inverse;
and because ofThus, μ 1/(e)HJl(p)-1e) Is thusThe child cost function for the ith observation slot is then:
fusing the extended covariance matrix of all observation time slots to construct a reduced-dimension cost function fRD-Capon(p):
6. The non-circular signal-oriented dimensionality reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring according to claim 5, wherein the detailed steps of the step 4) are as follows:
and searching the cost function after dimension reduction, wherein the coordinates corresponding to the Q maximum peak values are the positions of the non-circular radiation sources.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010959291.2A CN112180324B (en) | 2020-09-14 | 2020-09-14 | Dimension reduction processing direct positioning method for non-circular signals in unmanned aerial vehicle mobile monitoring |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010959291.2A CN112180324B (en) | 2020-09-14 | 2020-09-14 | Dimension reduction processing direct positioning method for non-circular signals in unmanned aerial vehicle mobile monitoring |
Publications (2)
Publication Number | Publication Date |
---|---|
CN112180324A true CN112180324A (en) | 2021-01-05 |
CN112180324B CN112180324B (en) | 2023-10-27 |
Family
ID=73920791
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010959291.2A Active CN112180324B (en) | 2020-09-14 | 2020-09-14 | Dimension reduction processing direct positioning method for non-circular signals in unmanned aerial vehicle mobile monitoring |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN112180324B (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113359086A (en) * | 2021-06-25 | 2021-09-07 | 南京航空航天大学 | Weighted subspace data fusion direct positioning method based on augmented co-prime array |
CN113391266A (en) * | 2021-05-28 | 2021-09-14 | 南京航空航天大学 | Direct positioning method based on non-circular multi-nested array dimensionality reduction subspace data fusion |
CN117249829A (en) * | 2023-08-23 | 2023-12-19 | 湖南六九零六信息科技股份有限公司 | Flight path planning method of unmanned aerial vehicle platform under direction-finding cross positioning system |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IL214988A0 (en) * | 2010-09-08 | 2012-02-29 | Thales Sa | Method for geolocating multiple emitters by space-time processing |
CN107290717A (en) * | 2017-05-19 | 2017-10-24 | 中国人民解放军信息工程大学 | For the direct localization method of multiple target of not rounded signal |
CN108445444A (en) * | 2018-03-09 | 2018-08-24 | 中国人民解放军战略支援部队信息工程大学 | Correct the direct localization method of volume Kalman filtering |
-
2020
- 2020-09-14 CN CN202010959291.2A patent/CN112180324B/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
IL214988A0 (en) * | 2010-09-08 | 2012-02-29 | Thales Sa | Method for geolocating multiple emitters by space-time processing |
CN107290717A (en) * | 2017-05-19 | 2017-10-24 | 中国人民解放军信息工程大学 | For the direct localization method of multiple target of not rounded signal |
CN108445444A (en) * | 2018-03-09 | 2018-08-24 | 中国人民解放军战略支援部队信息工程大学 | Correct the direct localization method of volume Kalman filtering |
Non-Patent Citations (3)
Title |
---|
尹洁昕: "基于阵列信号的目标直接定位方法研究", <中国博士学位论文全文数据库信息科技辑>, no. 1, pages 23 - 117 * |
张小飞 等: "电磁矢量互质阵中基于降维Capon的DOA和极化估计算法", 《数据采集与处理》, vol. 33, no. 6, pages 953 - 960 * |
逯志宇 等: "基于对称旋转不变性的非圆相干分布源直接定位算法", 《电子与信息学报》, vol. 41, no. 3, pages 537 - 542 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113391266A (en) * | 2021-05-28 | 2021-09-14 | 南京航空航天大学 | Direct positioning method based on non-circular multi-nested array dimensionality reduction subspace data fusion |
CN113359086A (en) * | 2021-06-25 | 2021-09-07 | 南京航空航天大学 | Weighted subspace data fusion direct positioning method based on augmented co-prime array |
CN117249829A (en) * | 2023-08-23 | 2023-12-19 | 湖南六九零六信息科技股份有限公司 | Flight path planning method of unmanned aerial vehicle platform under direction-finding cross positioning system |
Also Published As
Publication number | Publication date |
---|---|
CN112180324B (en) | 2023-10-27 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN112180324A (en) | Non-circular signal-oriented dimension reduction processing direct positioning method in unmanned aerial vehicle mobile monitoring | |
CN106054123B (en) | A kind of sparse L battle arrays and its arrival direction estimation method | |
CN101795150B (en) | Method for estimating direction of arrival and information source number of strong and weak signals | |
CN111965595A (en) | Multi-non-circular information source high-precision direct positioning method based on unmanned aerial vehicle | |
CN110824415B (en) | Sparse direction-of-arrival angle estimation method based on multiple-sending and multiple-receiving array | |
CN109655799A (en) | The non-homogeneous thinned array direction-finding method of covariance matrix vectorization based on IAA | |
CN107132505A (en) | The through direct localization method of multiple target with non-through mixing field scape | |
CN107290717B (en) | For the direct localization method of multiple target of non-circular signal | |
WO2020248443A1 (en) | Two-dimensional phased antenna array-based rapid and accurate beam tracking method | |
CN103777214B (en) | Non-stationary suppression jamming signal inhibition method in satellite navigation system | |
CN113835063B (en) | Unmanned aerial vehicle array amplitude and phase error and signal DOA joint estimation method | |
CN111337873A (en) | DOA estimation method based on sparse array | |
CN110286350A (en) | A kind of perfect match method and device of L-type Sparse Array DOA estimation | |
CN114554398A (en) | Indoor positioning method, first positioning server and indoor positioning system | |
CN114371445A (en) | Multi-radiation source direct positioning method based on single unmanned aerial vehicle | |
CN104168047A (en) | Single-ended time domain beam searching method based on compressed sensing | |
CN113075649A (en) | Signal level direct positioning method suitable for distributed networked radar | |
CN117991178A (en) | High-precision array direction finding method and system based on data driving | |
CN113625220A (en) | New method for quickly estimating direction of arrival and diffusion angle of multipath signal | |
CN115079119B (en) | DMIMO radar multi-target detection and positioning method for non-ideal orthogonal waveform | |
CN115219981A (en) | Non-circular information source direct positioning method based on dimension reduction propagation operator in distributed monitoring | |
CN113075621B (en) | Signal level positioning algorithm precision boundary calculation method for distributed networked radar | |
CN115052246A (en) | Broadband signal direct positioning method based on multi-frequency cost function fusion under unknown attenuation coefficient | |
Ichige et al. | An explicit high-resolution DOA estimation formula for two wave sources | |
CN117202343B (en) | Distributed array cooperative direct positioning method for multiple broadband signal radiation sources |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |